The collection and transformation of data and information from the Internet and social media into knowledge is a daunting challenge for system developers. “Information” is organized or structured data, whereas “knowledge” is the synthesis of multiple sources of information over time. Much of the difficulty in synthesizing multiple sources of data and information into knowledge is the inherent characteristics of the Internet and social media. Data and information are often unorganized, multi-modal, and distributed over server sites all over the world. The number and variety of data and information sources is constantly increasing. The same piece of data or information can often be accessible from many different data sources and in different formats. Data and information are often ambiguous and sometimes erroneous. Finally, some data and information are not consistently available for integration. For these reasons, the transformation from data and information into knowledge is not an easy task.
Search engines and related technologies fall short of transforming data and information into knowledge. Search engines do not possess a reasoning capability and as a result they return a great deal of irrelevant data and information. The step of filtering that data and information based on meaningful heuristics is left to the human operator. Search engines are not well-equipped to handle ambiguity and imprecise searches yield unexpected results.
Semantic computing engines, or knowledge engines, introduce reasoning to the data and information retrieval process. Knowledge engines employ machine learning algorithms to understand the intentions of users, and convert those intentions into machine-level search instructions. In addition, knowledge engines employ machine learning algorithms to understand the retrieved content, regardless of its form. Knowledge engines are capable of mapping the semantics of a particular user with various types of content.
A problem with existing knowledge engines is that they are often domain-specific and are based on different ontologies. The knowledge engines are not integrated and there is no seamless and coherent user interface.
Cloud computing systems and methods have been developed to provide dynamically scalable resources that can be allocated as a service to users over a network. An advantage of building a knowledge engine integration system on a cloud computing system is the abstraction of both physical resources and software implementations from the user. In addition, the interfaces between the knowledge engines and the disparate data and information sources can be managed through the cloud. For example, a user is only required to log into a cloud instance to have access to a multitude of data and information sources.
The present invention is directed to overcoming one or more of the problems set forth above.